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All Journal International Journal of Electrical and Computer Engineering HAYATI Journal of Biosciences Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences The Journal of Experimental Life Sciences (JELS) Biotropika Proceeding International Conference on Global Resource Conservation The Journal of Pure and Applied Chemistry Research Majalah Obat Tradisional Science and Technology Indonesia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences
Nashi Widodo
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Journal : International Journal of Electrical and Computer Engineering

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A hybrid feature selection on AIRS method for identifying breast cancer diseases Ridok, Achmad; Widodo, Nashi; Mahmudy, Wayan Firdaus; Rifa’i, Muhaimin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp728-735

Abstract

Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 times the crossing (CF) of our validation on various AIRS seeds indicate that the proposed method can achieve the best performance with accuracy =0.9797 and AUC=0.9777 at k=6 and seed=50.
Co-Authors Achmad Ridok Adi Hermansyah, Adi Agatha Mia Puspitasari Agatha Mia Puspitasari Amalia, Maulida Nisa Annisa, Yuslinda Aris Soewondo Azerlyn, Defiona Rensia Naomi Azzamuddin, Haidar Custer C. Deocaris Deocaris, Custer C. Dewi Mustikaningtyas Didik Priyandoko Didik Priyandoko Dinia Rizqi Dwijayanti Dodi Iskandar, Dodi Dwi Adi Nugroho Fairuz Sarah Kamila Fitria Eka Aprilia Grahadi, Rahmat Hafil Kusuma, Kavana Hermanto, Feri Eko Ichsan Ichsan Ika Christina, Yuyun Ikhtiarini, Nur Inggita Kusumastuty Johan Aloysius Juniardi, Muhamad Rizki Kamila, Fairuz Sarah Kaul, Sunil C. Kesuma, Suryanata Khansa, Shafanony Kholilah, Tsaniyah Nur Kusuma, Kavana Hafil Listijani Suhargo, Listijani Marhendra, Eunike Berliana Mariyah Ulfa, Siti Marlita Marlita, Marlita Masruri Masruri Mifetika Lukitasari Moch Sasmito Djati Mohammad Saifur Rohman Nafisah, Wirdatun Nia Kurniawan Nugroho, Inggil Cahyo Nur Ida Panca Nugrahini Oktavia Rahayu Adianingsih Puspitoputri, Alya H. raudatul jannah raudatul jannah Renu Wadhwa Rifa'i, Muhaimin Rifa’i, Muhaimin Rizqi Dwijayanti, Dinia Rohmah, Ilmiana Nurur Rollando, Rollando Rosyadah, Nuraini Roudlotul Jannah ROUDLOTUL JANNAH Salma Wahid Marseti Sapti Puspitarini Siti Fatiyatur Rahmah Siti Fatiyatur Rahmah Siti Mariyah Ulfa Siti Nurkasanah Sri Widyarti Sri Winarsih Sunil C. Kaul Sutiman B. Sumitro Ulinnuha, Maria VALENTINA YURINA Wadhwa, Renu Warsito Warsito Wayan Firdaus Mahmudy Widekdo, Dwi purno Widyananda, Muhammad Hermawan Yanma, Nadifa Almira YOGA DWI JATMIKO Yuslinda Annisa Yuyun Ika Christina